Applied ML Researcher (Force Fields and Simulation)

## ML Research Engineer (Machine Learning Force Fields)

Role: Member of Technical Staff focused on advancing molecular simulation capabilities through developing next-generation computational methods and robust infrastructure.

### Your Impact
You'll shape the simulation infrastructure that enables CuspAI to evaluate novel material candidates through atomistic physics. You'll bring simulations to the accuracy and performance needed to power large-scale search campaigns, and design them to be flexible and versatile for new challenges.

### What You Will Do

Models

• Train, fine-tune, and distill machine learning force fields
• Research and develop novel ML force field architectures suited to production simulation workloads

Systems & Infrastructure

• Integrate models into public and in-house high-performance simulators
• Develop training and inference architectures for large-scale training, data generation, and simulation
• Distribute workloads via Ray to scale across compute infrastructure
• Build modular systems so components can be reused across many kinds of chemistry

Science & Collaboration

• Build an active learning system that closes the loop between simulation, data generation, and training
• Develop interfaces that make the system easy for domain scientists to use and extend
• Collaborate closely with computational chemists on density functional theory (DFT) data generation and validation

### Must Have Skills and Qualifications

• Demonstrated technical excellence in both research and implementation with a track record of building high-quality, performant systems
• Exceptional coding skills with strong command of modern software engineering practices
• Deep production or research experience with distributed machine learning systems
• PhD (or comparable professional experience) in a relevant quantitative field (Computer Science, Physics, Applied Mathematics, Computational Science, Machine Learning) with strong foundation in computational methods
• Genuine and explicit interest in the potential applications of AI within materials science and chemistry

### Bonus Points

• Experience with deploying, training, and modifying machine learning force fields
• Experience with management of atomistic data
• Experience with Density Functional Theory
• Experience with molecular simulation methods (MCMC, MD)
• Experience with graph neural network design
• Experience with Cloud infrastructure and Kubernetes
• Track record of published research at top-tier venues in ML (NeurIPS, ICML) or computational physics